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Bulletin of Electrical Engineering and Informatics
Published by Universitas Ahmad Dahlan
ISSN : -     EISSN : -     DOI : -
Core Subject : Engineering,
Electrical & Electronics Engineering
Bulletin of Electrical Engineering and Informatics (Buletin Teknik Elektro dan Informatika) ISSN: 2089-3191, e-ISSN: 2302-9285 is open to submission from scholars and experts in the wide areas of electrical, electronics, instrumentation, control, telecommunication and computer engineering from the global world. The journal publishes original papers in the field of electrical, computer and informatics engineering.
Arjuna Subject : -
Articles 64 Documents
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Search results for , issue "Vol 11, No 5: October 2022" : 64 Documents clear
Development of methods and technologies for creating intelligent scientific and educational internet resources Ardak G. Batyrkhanov; Zhanna B. Sadirmekova; Madina A. Sambetbayeva; Assel N. Nurgulzhanova; Zhuldyz S. Ismagulova; Aigerim S. Yerimbetova
Bulletin of Electrical Engineering and Informatics Vol 11, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i5.3075

Abstract

The purpose of this work is to develop methods, technologies and tools for creating and maintaining intelligent scientific and educational internet resources (ISEIR) based on a service-oriented approach and Semantic Web technologies. The main purpose of ISEIR is to provide meaningful access to scientific and educational information resources of a given field of knowledge and integrated information processing services. According to the preliminary concept, an intelligent scientific and educational internet resource will be an information system accessible via the internet which provides ontology-based systematization and integration of scientific knowledge, data and information resources into a single information space together with a meaningful effective access to them as well as supporting their use in solving various scientific and educational tasks. ISEIR is equipped with an ergonomic web-based user interface and special editors designed to manage the knowledge integrated into it. The proposed approach to the construction of intelligent scientific and educational internet resources is the basis for the developed technology in creating and maintaining information environments for distributed learning.
Wideband improvement for hybrid plasmonic fractal patch nanoantenna Refat Taleb Hussein; Dheif Ibrahem Abood
Bulletin of Electrical Engineering and Informatics Vol 11, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i5.4129

Abstract

A wideband improvement for hybrid plasmonic fractal patch nanoantenna is presented for use in intra/inter chip optical interconnects. The suggested fractal patch antenna covering a part of U-band (1625-1675), L-band (1565-1625nm), C-band (1530-¬1565nm), S-band (1460-1530nm), E-band (1360-1460nm) and most of O-band (1260-1360nm) optical communication bands. The proposed antenna has a promising future use in inter and intra chip optical communications to eliminate electrical interconnection limitations such as interconnect density, power consumption and also increasing data rate. The performance of this antenna has been evaluated using full wave simulation computer simulation technology (CST) Microwave software. The impedance bandwidth is largely enhanced by applying rectangular fractal cuts to the both sides of patch. The proposed antenna achieves a wider bandwidth from 168 THz to 228 THz (B.W.=60 THz), which is about 8 times greater the bandwidth of reference antenna with a good gain and more than 95% radiation efficiency throughout the operational bandwidth.
DDoS attack detection in software defined networking controller using machine learning techniques Abbas Jasem Altamemi; Aladdin Abdulhassan; Nawfal Turki Obeis
Bulletin of Electrical Engineering and Informatics Vol 11, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i5.4155

Abstract

The term software defined networking (SDN) is a network model that contributes to redefining the network characteristics by making the components of this network programmable, monitoring the network faster and larger, operating with the networks from a central location, as well as the possibility of detecting fraudulent traffic and detecting special malfunctions in a simple and effective way. In addition, it is the land of many security threats that lead to the complete suspension of this network. To mitigate this attack this paper based on the use of machine learning techniques contribute to the rapid detection of these attacks and methods were evaluated detecting DDoS attacks and choosing the optimum accuracy for classifying these types within the SDN, the results showed that the proposed system provides the better results of accuracy to detect the DDos attack in SDN network as 99.90% accuracy of Decision Tree (DT) algorithm.
Deep learning algorithms to improve COVID-19 classification based on CT images Hamza Abu Owida; Hassan S. Migdadi; Omar Salah Mohamed Hemied; Nawaf Farhan Fankur Alshdaifat; Suhaila Farhan Ahmad Abuowaida; Rami S. Alkhawaldeh
Bulletin of Electrical Engineering and Informatics Vol 11, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i5.3802

Abstract

In response to the growing threat posed by COVID-19, several initiatives have been launched to develop methods of halting the progression of the disease. In order to diagnose the COVID-19 infection, testing kits were utilized; however, the use of these kits is time-consuming and suffers from a lack of quality control measures. Computed tomography is an essential part of the diagnostic process in the treatment of COVID-19 (CT). The process of disease detection and diagnosis could be sped up with the help of automation, which would cut down on the number of exams that need to be carried out. A number of recently developed deep learning tools make it possible to automate the Covid-19 scanning process in CT scans and provide additional assistance. This paper investigates how to quickly identify COVID-19 using computational tomography (CT) scans, and it does so by using a deep learning technique that is derived from improving ResNet architecture. In order to test the proposed model, COVID-19 CT scans that include a patient-based split are utilized. The accuracy of the model’s core components is 98.1%, with specificity at 97% and sensitivity at 98.6%.
In Malang, Indonesia, a techno-economic analysis of hybrid energy systems in public buildings Mochammad Junus; Marjono Marjono; Aulanni’am Aulanni’am; Slamet Wahyudi
Bulletin of Electrical Engineering and Informatics Vol 11, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i5.3795

Abstract

Because of the fickle nature of the renewable sources of energy production, professionals in this sector have developed hybrid renewable energy systems (HRES) that offer a constant and stable load supply. This research intends to build off-grid hybrid energy systems (HES) in Malang, Indonesia, that uses a solar generator, wind turbine, and biogas to power public buildings. The HOMER program was used to construct this model. Following the computations, multiple hybrid renewable energy system models are used to analyze each component’s capital cost and also cost of energy (COE). Furthermore, energy output, gas emissions, and a thermoeconomic assessment of several HRES models have been explored. Two ideal HRES models were evaluated: one with a biogas generator and one without. According to the research, employing a generator of biogas would reduce fuel consumption and emissions by 68.3%. This HRES model is impressive in light of Malang’s severe air pollution. Switching from diesel to biogas generator decreases NPC by 6.84%, according to the data.
An automated approach for eggplant disease recognition using transfer learning Izazul Haque Saad; Md. Mazharul Islam; Isa Khan Himel; Md. Jueal Mia
Bulletin of Electrical Engineering and Informatics Vol 11, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i5.3575

Abstract

In Bangladesh, eggplant is a widely grown crop that is vital to the country’s food security. The vegetable is consumed on a regular basis by the majority of people. Since Bangladesh’s economy is heavily reliant on agriculture, eggplant growing might help the country’s economy and productivity flourish more efficiently. It provides several health benefits, including reducing cancer risk, blood sugar control, heart health, eye health, and others. Although eggplant is a valuable crop, it is subject to severe diseases that reduce its productivity. It’s hard to detect those diseases manually and needs a lot of time and hard work. So, we introduce an agricultural and medical expert system based on machine vision that analyzes a picture acquired with a smartphone or portable device and classifies diseases to assist farmers in resolving the issue. We studied and used a convolutional neural network (CNN)-based transfer learning approach for identifying eggplant diseases in this paper. We have used various transfer learning models such as DenseNet201, Xception, and ResNet152V2. DenseNet201 had the highest accuracy of these models with 99.06%.
Embedded control unit design for energy management in smart homes Rawan Mazen Abusharia; Kasim Mousa Al-Aubidy
Bulletin of Electrical Engineering and Informatics Vol 11, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i5.4103

Abstract

This paper deals with smart home energy management through load scheduling and optimal use of available energy sources. In this study, three energy sources were considered: the national electricity grid, photovoltaic (PV) energy, and the storage unit. The PV array can provide the maximum power to the load at a given operating point where the output power changes with temperature, radiation and load. Therefore, a real-time controller is proposed to track the maximum power. An energy management algorithm has been proposed in a smart home to achieve the main goal of making the electricity bill as low as possible. The algorithm involves scheduling loads by assigning a priority to each load. The loads are supplied with the required power according to their priorities and the available energy. The obtained results indicate that supplying the PV system with a fuzzy-based MPPT indicates an increase in system efficiency. The results also showed that the use of energy management based on load scheduling led to a significant reduction in the electricity bill.
Modeling and parameter estimation of solar photovoltaic based MPPT control using EKF to maximize efficiency Rachid Kerid; Younes Bounnah
Bulletin of Electrical Engineering and Informatics Vol 11, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i5.3782

Abstract

In this paper, we focus on the design, modeling and implementation of a MPPT controller based maximum power tracking of photovoltaic system. The electrical characteristic of The PV system is non-linear and changes with the solar irradiation and the ambient temperature. Therefore, the incremental conductance (IC) method control is known for its stability and robustness, and is used to extract the maximum energy from the PV source using a boost converter topology. It provides a strong basis for the improvement and optimization of control parameters of a photovoltaic system. Implementing MPPT algorithm usually need the use of a lot of sensors if accuracy of the system has to be increased. However, IC method with an extended Kalman filter (EKF) can be utilized in order to estimate some parameters to reduce the number of Sensors. The EKF is deployed in the optimal position to estimate both current and the capacitor voltage, thus allowing to eliminate two sensors devise from the entire PV system, which increases the system efficiency and reliability, simplifies the control method and decreases the system cost. The performance of the proposed technique is validated by experimental and simulation results under different operating conditions and load changes.
Effect of peak sun hour on energy productivity of solar photovoltaic power system Prisma Megantoro; Muhammad Akbar Syahbani; Irfan Helmi Sukmawan; Sigit Dani Perkasa; Pandi Vigneshwaran
Bulletin of Electrical Engineering and Informatics Vol 11, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i5.3962

Abstract

A solar cell is a type of renewable energy engineering technology that can convert photons coming from the sun to be converted into electrical energy. The amount of energy that can be converted by a solar cell is determined by the effective insolation time. Peak sun hours (PSH) are the focus of this research. This PSH analysis aims to determine the potential for solar energy obtained in geographical locations throughout the year. Geographical location and the position of the astronomical coordinates of a certain area affect PSH. Therefore, the orientation of solar panel installation, including the height, slope, and latitude of the solar panel surface needs to be considered in order to get maximum solar energy. The results of this study can be used by technicians in determining the orientation of solar panel development in an area.
Comparison between boost and positive output super lift Luo converters to improve the performance of photovoltaic system Ream Mohammed Jassim; Kadhim H. Hassan; Issa Ahmed Abed
Bulletin of Electrical Engineering and Informatics Vol 11, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i5.3941

Abstract

Employment DC-DC switching power converters in many different areas become is very important. In this paper, three different models of the photovoltaic solar module were proposed in order to designing, implementation, and simulated them in MATLAB/Simulink with the boost converter circuit first and then with the positive output super lift Luo (POSLL) converter circuit again. A comparison was made between the two circuits, as well as a theoretical and simulation values were made and compared between them (in the same standard conditions) for each of these selected models. So as to improving solar system performance and clarify the functions played by POSLL in power electronic circuits.

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